Asset Tracking Device Predictive Reporting
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Solution Overview
Problem
Existing asset tracking systems face challenges in efficiently managing power consumption, particularly when establishing connections with wireless networks and sending periodic reports, which can lead to battery depletion in battery-powered devices.
Innovation Solution
The implementation of an asset tracking device that uses predicted location information to determine whether to send current transport status reports, thereby reducing unnecessary power consumption by skipping reports when predicted statuses are sufficiently accurate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the asset tracking device sends periodic reports of current transport status, then the tracking accuracy and reliability are improved, but the power consumption increases and battery life decreases
Solution Approach 1:
The system performs preliminary prediction of future transport status using historical data and machine learning models before actually sending reports. By predicting what the status will be at future reporting times, the system can proactively determine whether to send reports, avoiding unnecessary power consumption while maintaining tracking accuracy when changes occur.
Solution Approach 2:
The system continuously monitors actual transport status against predicted status and uses this feedback to adjust reporting behavior. When the actual status deviates from predictions beyond a threshold, the system sends reports to update tracking. This feedback mechanism ensures reliability is maintained only when necessary, reducing unnecessary power consumption.
2Reliability
If the asset tracking device establishes connections with wireless networks frequently, then the reporting reliability is improved, but the power consumption increases
Solution Approach 1:
The system preliminarily determines whether a network connection is needed by comparing predicted vs. actual transport status before establishing wireless connections. This preliminary assessment prevents unnecessary connection establishment, reducing power consumption while ensuring connections are made only when status changes require reporting.
Solution Approach 2:
The asset tracking device autonomously determines when to establish network connections based on its own prediction of status changes, without requiring external instructions. This self-service approach optimizes the balance between connection reliability and power consumption based on real-time conditions.
3Measurement precision
If the asset tracking device sends more frequent reports, then the tracking precision is improved, but the battery life decreases
Solution Approach 1:
The system performs preliminary prediction of future transport status to determine optimal reporting frequency. By predicting when status changes are likely to occur, the system can space reports strategically to maintain precision without unnecessary frequent updates, thereby extending battery life.
Solution Approach 2:
The reporting frequency is dynamically adjusted based on predicted and actual transport status. When the system predicts significant changes are upcoming or actual changes occur, reporting frequency increases to maintain precision. When status is stable, reporting frequency decreases, extending battery life.
Data Source
AI summary
In some examples, a device includes a processor configured to compare a current transport status of the asset to a predicted transport status of the asset at each respective time instance of a plurality of time instances, and in response to determining that the current transport status does not differ from the predicted transport status by greater than a specified threshold, skip sending a report relating to the current transport status to a service over a network at the respective time instance of the plurality of time instances.